MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Nothing to run from here
Brainiall NLP is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
22
tools declared
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trust score
remote
at its publisher
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calls last 7d
What it does
Sentiment, toxicity, entity extraction, PII, translation, summary, QA, fraud scoring, safety audit.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --transport http brainiall-nlp-mcp https://api.brainiall.com/mcp/nlp/mcp
# 2 — ask your agent for something these tools do
# analyze_toxicity, analyze_sentiment, extract_entities and 19 more
Brainiall NLP costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
BRAll 6 servers
brainiall6 servers
5 categories
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. Nothing here runs on your machine and nothing runs on ours: this server answers at its publisher’s own address, and what you install is the connection straight to it.
Claude Codedeclared by this listing
This is a public server: it answers at its publisher’s own address and this marketplace is not in the path. Claude Code registers it in one command; --scope user makes it available in every project. The transport below is http, as the publisher's own manifest declares it.
claude mcp add --scope user --transport http brainiall-nlp-mcp https://api.brainiall.com/mcp/nlp/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it answers at its publisher’s own address: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it writes
One config entry pointing straight at the publisher’s own address — this marketplace is not in the path, and nothing from this listing executes on your machine.
What you approve
22 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Analyze text for toxic content. Returns scores for 6 categories: toxic, severe_toxic, obscene, threat, insult, identity_hate. Each score is 0.0-1.0. BERT-based classifier with sub-15ms latency on GPU. Args: text: Text to analyze for toxicity (hate speech, insults, threats). Returns: dict with keys: - toxic (float 0-1): Overall toxicity score - severe_toxic (float 0-1): S
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Text to analyze for toxicity (hate speech, insults, threats) |
Analyze text sentiment. Returns positive/negative classification with confidence scores. Brainiall Sentiment engine-based with sub-10ms latency. Multiple domain-specific model variants available. Args: text: Text to analyze for sentiment (positive/negative). model: Model variant -- 'general' (default), 'financial', 'twitter'. Returns: dict with keys: - label (str): 'positive
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Text to analyze for sentiment (positive/negative) |
| model | string | no | Model variant: 'general' (default), 'financial', 'twitter' |
Extract named entities (NER) from text. Identifies persons, organizations, locations, and miscellaneous entities with span offsets and confidence scores. BERT-NER based with sub-50ms latency. Args: text: Text to extract named entities from. Returns: dict with keys: - entities (list): Detected entities, each containing: - text (str): Entity text - label (s
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Text to extract named entities from (persons, organizations, locations) |
Detect personally identifiable information (PII) in text. Finds emails, phone numbers, SSNs, credit cards, IP addresses, and person names. Optionally returns redacted text with PII replaced by type labels (e.g. [EMAIL], [PHONE]). BERT-NER + regex ensemble. Args: text: Text to scan for personally identifiable information. redact: If true, return redacted text with PII replaced by [TYPE].
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Text to scan for personally identifiable information |
| redact | boolean | no | If true, return redacted text with PII replaced by [TYPE] |
Detect the language of text. Supports 176 languages using fastText. Sub-1ms inference latency. Returns ISO 639-1 codes with confidence scores. Args: text: Text to identify the language of. top_k: Number of top language predictions to return (default: 3). Returns: dict with keys: - language (str): Top predicted language ISO 639-1 code - confidence (float 0-1): Confide
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Text to identify the language of |
| top_k | integer | no | Number of top language predictions to return |
Check health status of NLP API services and loaded models. Returns: dict with keys: - status (str): 'healthy' or error state - models (dict): Loaded model status per capability - version (str): API version
No parameter schema on file.
Translate text between 100+ languages. Args: text: The text to translate. target_lang: Target language code. source_lang: Source language code; omit to auto-detect. Returns: dict with the translated text (key: translated_text) and the detected source language if auto-detected.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | The text to translate |
| target_lang | string | yes | Target language code (e.g. 'pt', 'es', 'fr', 'de', 'ja') |
| source_lang | no | Source language code; omit to auto-detect |
Summarize text — extractive (verbatim key sentences in original order) or abstractive (concise rewrite). Args: text: The text to summarize. mode: 'abstractive' or 'extractive'. max_length: Target maximum length of the summary, in words. Returns: dict with the summary (key: summary) plus word/char counts.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | The text to summarize |
| mode | string | no | 'abstractive' (concise rewrite) or 'extractive' (most important sentences, verbatim) |
| max_length | integer | no | Target maximum length of the summary, in words |
Answer a question using ONLY the supplied text; returns the supporting sentence(s) with character offsets. Replies found:false rather than guessing when the answer isn't present in the text. Args: text: The text/document to answer from. question: The question to answer. Returns: dict with keys: answer (str|null), found (bool), supporting_spans (list of {text, start, end}).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | The text/document to answer from |
| question | string | yes | The question to answer |
Ingest a document into a knowledge base: it is chunked, embedded and stored for you (managed RAG). Args: namespace: The knowledge-base namespace. text: The document text. title: Optional title. Returns: dict with keys: doc_id (str), n_chunks (int).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| namespace | string | yes | The knowledge-base namespace to ingest into (alphanumeric/hyphen) |
| text | string | yes | The document text to ingest |
| title | no | Optional title for the document |
Retrieve the most relevant passages from a knowledge base plus (optionally) a grounded, cited answer. Returns found:false rather than a guess when the passages don't contain the answer. Args: namespace: The knowledge-base namespace. question: The natural-language question. top_k: How many passages to retrieve. rerank: Re-order retrieved passages before answering. synthesize:
| Parameter | Type | Required | Notes |
|---|---|---|---|
| namespace | string | yes | The knowledge-base namespace to query |
| question | string | yes | The natural-language question |
| top_k | integer | no | How many passages to retrieve |
| rerank | boolean | no | Re-order the retrieved passages before answering |
| synthesize | boolean | no | Also return a concise answer grounded only in the retrieved passages |
List the documents stored in a knowledge base (most recent first). Args: namespace: The knowledge-base namespace. Returns: dict with keys: documents (list of {doc_id, title, ...}).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| namespace | string | yes | The knowledge-base namespace |
Score a transaction or account event for fraud risk. Send whatever signals you have — all optional. Returns a 0-1 fraud probability, a risk level, the exact risk factors that drove the score (each with its weight, direction and a human-readable detail), and a recommended decision (allow|review|deny). Returns: dict with keys: fraud_probability (float), risk_level (str), decision (str), risk_s
| Parameter | Type | Required | Notes |
|---|---|---|---|
| amount | no | The transaction amount | |
| currency | no | ISO 4217 currency code | |
| avg_txn_amount_30d | no | The account's avg transaction amount over the last 30 days (for amount-anomaly scoring) | |
| txn_count_1h | no | Number of transactions on this account in the last hour | |
| txn_count_24h | no | Number of transactions on this account in the last 24h | |
| distinct_cards_24h | no | Distinct cards used on this account in 24h | |
| distinct_countries_24h | no | Distinct countries seen on this account in 24h | |
| is_new_device | no | First time seeing this device | |
| is_new_ip | no | First time seeing this IP | |
| is_tor | no | Request originated from a Tor exit node | |
| is_proxy_or_vpn | no | Request originated from a proxy/VPN/datacenter IP | |
| card_country | no | ISO country code of the payment instrument | |
| ip_country | no | ISO country code geolocated from the IP | |
| account_age_days | no | Age of the account in days | |
| avs_match | no | Whether the address-verification check matched | |
| cvv_provided | no | Whether the CVV was provided | |
| prior_chargebacks | no | Number of prior chargebacks on this account | |
| event_id | no | Your identifier for this event (echoed back; use with fraud_feedback) |
Report the confirmed outcome of an event so the fraud model can be re-calibrated to your data. Args: event_id: The event identifier. label: 'fraud' | 'legitimate' | 'chargeback' | 'dispute'. notes: Optional free-text notes. Returns: dict with keys: event_id (str), label (str), accepted (bool), feedback_id (int).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| event_id | string | yes | The event_id you passed to fraud_score (or your own identifier) |
| label | string | yes | The confirmed outcome: 'fraud' | 'legitimate' | 'chargeback' | 'dispute' |
| notes | no | Optional free-text notes |
Statistical key-phrase extraction — top-N ranked phrases. Brainiall Key Phrases engine. Pure-statistical (TF + position + casing + stopword filter), no ML cost.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Input text |
| top_k | integer | no | Number of phrases to return |
| max_ngram | integer | no | Max words per phrase (1-4) |
Sentiment per aspect. Brainiall Aspect Sentiment engine. Splits the text into sentences mentioning each aspect, classifies each, aggregates.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Input text |
| aspects | array | yes | Aspect terms to score (e.g. ['camera','battery','price']) |
Zero-shot text classification — define your labels at call time. No training, no data upload. Brainiall Custom Classifier engine. Returns {top_label, scores, confidence}.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Input text |
| labels | array | yes | Your candidate labels (2-20 of them) |
| multi_label | boolean | no | If True, multiple labels can apply |
Named-entity recognition + canonical linking to Wikidata Q-ids. Brainiall Entity Linker engine. Disambiguates 'Apple' the company from 'apple' the fruit.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Input text |
| max_entities | integer | no | Max entities to return |
Multi-turn PII detection with cross-turn coreference. Brainiall Conversational PII engine. Same surface text + type across turns gets the same entity_id.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| turns | array | yes | List of [role, content] dicts representing a dialogue |
Classify a prompt before it reaches your LLM. Brainiall Prompt Shield engine. Returns category (jailbreak | prompt_injection | data_exfiltration | impersonation | none), severity, reason, confidence.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| prompt | string | yes | The prompt text to classify (NOT executed) |
Hallucination check: is a claim actually supported by a source text? Brainiall Groundedness engine. Returns {grounded, confidence, supporting_span, reason}.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| claim | string | yes | The claim to verify |
| source | string | yes | The source text the claim should be grounded in |
Detect copyrighted text in user input — famous lyrics, literary openings, proprietary code. Brainiall Protected Material engine. Returns matched spans with source attribution.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | Text to scan for copyrighted material |
Free
$0 forever
- Every tool — any call limit is its publisher’s, not ours
- No card, and mcprush charges nothing for it
- Runs at its publisher’s own address — this marketplace is not in the path
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Brainiall NLP or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
—Grade not published
No rule-by-rule report on file for this release
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
This listing is an address its publisher runs, and you connect to it directly — this marketplace is not in the path. The scanner reads packages and public source, and an address is neither. So there is no grade — not a withheld one, an unmeasured one.
Release history
This is an address its publisher runs: which release it serves is theirs to choose, so there is nothing here to pin. The history below is for reading; the current release on file is 1.0.0.
1.0.0
undated
latestNo release note was published with this version.
—
no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish Brainiall NLP can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
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Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.